Survey on the Acceptance of Ambient Sensors in Solo and Older Couple-Only Households in Japan.

For this study, an online survey was conducted to discover the preferences of older adults when they used sensors in their households, rather than the preferences of the researchers who developed them. The sample size was 400 Japanese community-dwelling people aged 65 years and older. The numbers of samples for men and women, household composition (single-person/couple-only household), and younger senior (younger than 74 years old) and older senior (older than 75 years old) were equally assigned. The survey results showed that "informational security" and "constancy of life" were considered more important than other factors when installing sensors. Furthermore, looking at the results regarding the type of sensors that face resistance, we found that both cameras and microphones were evaluated as facing slightly strong resistance, while doors/windows, temperature/humidity, CO2/gas/smoke, and water flow were evaluated as not facing such strong resistance. The elderly who are likely to need sensors in the future also have various attributes, and the introduction of ambient sensors in elderly households may be further advanced by recommending applications that are easy to introduce based on the attributes of the target population, rather than discussing all of them in general.

Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features.

The number of people with dementia is increasing each year, and early detection allows for early intervention and treatment. Since conventional screening methods are time-consuming and expensive, a simple and inexpensive screening is expected. We created a standardized intake questionnaire with thirty questions in five categories and used machine learning to categorize older adults with moderate and mild dementia and mild cognitive impairment, based on speech patterns. To evaluate the feasibility of the developed interview items and the accuracy of the classification model based on acoustic features, 29 participants (7 males and 22 females) aged 72 to 91 years were recruited with the approval of the University of Tokyo Hospital. The MMSE results showed that 12 participants had moderate dementia with MMSE scores of 20 or less, 8 participants had mild dementia with MMSE scores between 21 and 23, and 9 participants had MCI with MMSE scores between 24 and 27. As a result, Mel-spectrogram generally outperformed MFCC in terms of accuracy, precision, recall, and F1-score in all classification tasks. The multi-classification using Mel-spectrogram achieved the highest accuracy of 0.932, while the binary classification of moderate dementia and MCI group using MFCC achieved the lowest accuracy of 0.502. The FDR was generally low for all classification tasks, indicating a low rate of false positives. However, the FNR was relatively high in some cases, indicating a higher rate of false negatives.

Research Participant Selection Bias in the Workshop Using Socially Assistive Robots for Older Adults and Its Effect on Population Representativeness.

Every research participant has their own personality characteristics. For example, older adults assisted by socially assistive robots (SAR) may have their own unique characteristics and may not be representative of the general population of older adults. In this research, we compared the average personality characteristics of participants in a workshop on robotics recruited directly through posting with those of older Japanese adults to examine participant selection bias and group representativeness for future study of SARs. After a one-week recruitment period, the workshop was attended by 20 older participants (nine males and 11 females) aged between 62 and 86 years. Extroversion among workshop participants was 4.38, 0.40 higher than the average for older adults in Japan. The workshop participants' openness was 4.55, 1.09 higher than the average for the Japanese elderly. Thus, the results indicate a slight selection bias in the personal characteristics of the participants depending on the recruitment method when compared to the Japan national average for older adults. In addition, only one of 20 participants was below the cutoff on the LSNS-6 score and considered to have a tendency toward social isolation. The development and introduction of socially assistive robots is often being considered to support people in social isolation in their daily lives; however, the results of this study showed that it is difficult to recruit people who tend to be socially isolated when gathering research participants by methods such as posting. Therefore, the effectiveness of the method of recruiting participants should be carefully verified in research regarding socially assistive robots.

Assessment of adjunct cognitive functioning through intake interviews integrated with natural language processing models.

In this article, we developed an interview framework and natural language processing model for estimating cognitive function, based on an intake interview with psychologists in a hospital setting. The questionnaire consisted of 30 questions in five categories. To evaluate the developed interview items and the accuracy of the natural language processing model, we recruited participants with the approval of the University of Tokyo Hospital and obtained the cooperation of 29 participants (7 men and 22 women) aged 72-91 years. Based on the MMSE results, a multilevel classification model was created to classify the three groups, and a binary classification model to sort the two groups. For each of these models, we tested whether the accuracy would improve when text augmentation was performed. The accuracy in the multi-level classification results for the test data was 0.405 without augmentation and 0.991 with augmentation. The accuracy of the test data in the results of the binary classification without augmentation was 0.488 for the moderate dementia and mild dementia groups, 0.767 for the moderate dementia and MCI groups, and 0.700 for the mild dementia and MCI groups. In contrast, the accuracy of the test data in the augmented binary classification results was 0.972 for moderate dementia and mild dementia groups, 0.996 for moderate dementia and MCI groups, and 0.985 for mild dementia and MCI groups.

Cognitive Assessment of Japanese Older Adults with Text Data Augmentation.

Current medical science has not yet found a cure for dementia. The most important measures to combat dementia are to detect the tendency toward cognitive decline as early as possible and to intervene at an early stage. For this reason, screening for dementia based on language ability has attracted much attention in recent years. However, in most of the previous studies, the cohort of people with dementia has been smaller than the control cohort. In this paper, we use a pre-trained Japanese language model for text analysis and evaluate the effectiveness of text augmentation on a dataset consisting of Japanese-speaking healthy older adults and those with mild cognitive impairment (MCI). We also examined what tasks contributed to the results. This experimental setting can also be used to detect other diseases that may affect the language areas of the brain outside of the hospital.

Eliciting a User's Preferences by the Self-Disclosure of Socially Assistive Robots in Local Households of Older Adults to Facilitate Verbal Human-Robot Interaction.

To realize a society in which older adults can live independently in their homes and familiar environments for as long as possible, their lives can be supported by providing appropriate technology. In this case, a new intervention for older people using socially assistive robots (SARs) is proposed; however, previous research has demonstrated that individual differences exist in the use and response to SAR interventions, and it has also been reported that SARs are not used by users in some cases. Therefore, in this study, we developed a self-disclosure function to promote continuous interaction with robots, using a Japanese corpus and self-disclosure items. In this study, we defined the specific requirements and functions of self-disclosure in SARs and developed ten non-arbitrary speech scripts from the field of social psychology using a Japanese corpus and self-disclosure items. To evaluate the effect of self-disclosure in SARs, an SAR was introduced to each household for 20 days, with the consent of seven community-dwelling older adults. Based on the recorded voice interaction data, we analyzed how the number, total time, and quality of verbal interactions changed with the SAR's self-disclosure. Furthermore, we conducted group interviews with the participants and received positive comments regarding the robot's self-disclosure. Some participants considered the specific personality of the SAR by accumulating its behavioral characteristics. As a consequence, these results indicate that the robot's self-disclosure feature is effective in significantly increasing the quantity and quality of verbal interactions with older adults.